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							- from typing import Optional
 - 
 - from core.rag.datasource.keyword.keyword_factory import Keyword
 - from core.rag.datasource.vdb.vector_factory import Vector
 - from core.rag.models.document import Document
 - from models.dataset import Dataset, DocumentSegment
 - 
 - 
 - class VectorService:
 -     @classmethod
 -     def create_segments_vector(
 -         cls, keywords_list: Optional[list[list[str]]], segments: list[DocumentSegment], dataset: Dataset
 -     ):
 -         documents = []
 -         for segment in segments:
 -             document = Document(
 -                 page_content=segment.content,
 -                 metadata={
 -                     "doc_id": segment.index_node_id,
 -                     "doc_hash": segment.index_node_hash,
 -                     "document_id": segment.document_id,
 -                     "dataset_id": segment.dataset_id,
 -                 },
 -             )
 -             documents.append(document)
 -         if dataset.indexing_technique == "high_quality":
 -             # save vector index
 -             vector = Vector(dataset=dataset)
 -             vector.add_texts(documents, duplicate_check=True)
 - 
 -         # save keyword index
 -         keyword = Keyword(dataset)
 - 
 -         if keywords_list and len(keywords_list) > 0:
 -             keyword.add_texts(documents, keywords_list=keywords_list)
 -         else:
 -             keyword.add_texts(documents)
 - 
 -     @classmethod
 -     def update_segment_vector(cls, keywords: Optional[list[str]], segment: DocumentSegment, dataset: Dataset):
 -         # update segment index task
 - 
 -         # format new index
 -         document = Document(
 -             page_content=segment.content,
 -             metadata={
 -                 "doc_id": segment.index_node_id,
 -                 "doc_hash": segment.index_node_hash,
 -                 "document_id": segment.document_id,
 -                 "dataset_id": segment.dataset_id,
 -             },
 -         )
 -         if dataset.indexing_technique == "high_quality":
 -             # update vector index
 -             vector = Vector(dataset=dataset)
 -             vector.delete_by_ids([segment.index_node_id])
 -             vector.add_texts([document], duplicate_check=True)
 - 
 -         # update keyword index
 -         keyword = Keyword(dataset)
 -         keyword.delete_by_ids([segment.index_node_id])
 - 
 -         # save keyword index
 -         if keywords and len(keywords) > 0:
 -             keyword.add_texts([document], keywords_list=[keywords])
 -         else:
 -             keyword.add_texts([document])
 
 
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